Unsupervised pharmacophore modeling combined with QSAR analyses revealed novel low micromolar SIRT2 inhibitors

Discovery of new potent lysine specific histone demythelase-1 inhibitors (LSD-1) using structure based and ligand based molecular modelling and machine learning

Lysine-specific histone demethylase 1 (LSD-1) is an epigenetic enzyme that oxidatively cleaves methyl groups from monomethyl and dimethyl Lys4 of histone H3 and is highly overexpressed in different types of cancer. Therefore, it has been widely recognized as a promising therapeutic target for cancer therapy. Towards this end, we employed various Computer Aided Drug Design (CADD) approaches including pharmacophore modelling and machine learning. Pharmacophores generated by structure-based (SB) (either crystallographic-based or docking-based) and ligand-based (LB) (either supervised or unsupervised) modelling methods were allowed to compete within the context of genetic algorithm/machine learning and were assessed by Shapley additive explanation values (SHAP) to end up with three successful pharmacophores that were used to screen the National Cancer Institute (NCI) database. Seventy-five NCI hits were tested for their LSD-1 inhibitory properties against neuroblastoma SH-SY5Y cells, pancreatic carcinoma Panc-1 cells, glioblastoma U-87 MG cells and in vitro enzymatic assay, culminating in 3 nanomolar LSD-1 inhibitors of novel chemotypes.

Structure-Based Pharmacophore Screening Coupled with QSAR Analysis Identified Potent Natural-Product-Derived IRAK-4 Inhibitors

Interleukin-1 Receptor-Associated Kinase 4 (IRAK-4) has crucial functions in inflammation, innate immunity, and malignancy. Structure-based pharmacophore modeling integrated with validated QSAR analysis was implemented to discover structurally novel IRAK-4 inhibitors from natural products database. The QSAR model combined molecular descriptors with structure-based pharmacophore capable of explaining bioactivity variation of structurally diverse IRAK-4 inhibitors. Manually built pharmacophore model, validated with receiver operating characteristic curve, and selected using the statistically optimum QSAR equation, was applied as a 3D-search query to mine AnalytiCon Discovery database of natural products. Experimental in vitro testing of highest-ranked hits identified uvaretin, saucerneol, and salvianolic acid B as active IRAK-4 inhibitors with IC₅₀ values in low micromolar range.

Discovery of potent IRAK-4 inhibitors as potential anti-inflammatory and anticancer agents using structure-based exploration of IRAK-4 pharmacophoric space coupled with QSAR analyses

Interleukin-1 Receptor-Associated Kinase 4 (IRAK-4) has an important role in immunity, inflammation, and malignancy. The significant role of IRAK-4 makes it an interesting target for the discovery and development of potent small molecule inhibitors. In the current study, multiple linear regression -based QSAR analyses coupled with structure-based pharmacophoric exploration was applied to reveal the structural and physiochemical properties required for IRAK-4 inhibition. Manually built pharmacophoric models were initially validated with receiver operating characteristic curve, and best-ranked models were subsequently integrated in QSAR analysis along with other physiochemical descriptors. The pharmacophore model, selected using the statistically optimum QSAR equation, was implied as a 3D-search filter to mine the National Cancer Institute database for novel IRAK-4 inhibitors. Whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent IRAK-4 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an IC₅₀ value of 157 nM.